Typos from @dmi3kno.

Fixes #26

transform.Rmd

@@ -24,7 +24,7 @@ When working with data you must:
 1. Figure out what you want to do.
 
 1. Precisely describe what you want to do in such a way that the
-   compute can understand it (i.e. program it).
+   computer can understand it (i.e. program it).
 
 1. Execute the program.
 
@@ -67,7 +67,7 @@ It prints differently because it has a different "class" to usual data frames:
 class(flights)
 ```
 
-This is called a `tbl_df` (pronounced tibble diff) or a `data_frame` (pronounced "data underscore frame"; cf. `data dot frame`). Generally, however, we want worry about this relatively minor difference and will refer to everything as data frames.
+This is called a `tbl_df` (pronounced "tibble diff") or a `data_frame` (pronounced "data underscore frame"; cf. `data dot frame`). Generally, however, we won't worry about this relatively minor difference and will refer to everything as data frames.
 
 You'll learn more about how that works in data structures. If you want to convert your own data frames to this special case, use `as.data_frame()`. I recommend it for large data frames as it makes interactive exploration much less painful.
 
@@ -83,7 +83,7 @@ There are two other important differences between tbl_dfs and data.frames:
 
 *   When you subset a tbl\_df with `[`, it always returns another tbl\_df. 
     Contrast this with a data frame: sometimes `[` returns a data frame and
-    sometimes it just returns a single column:
+    sometimes it just returns a single column (i.e. a vector):
     
     ```{r}
     df1 <- data.frame(x = 1:3, y = 3:1)
@@ -95,7 +95,7 @@ There are two other important differences between tbl_dfs and data.frames:
     class(df2[, 1])
     ```
     
-    To extract a single column use `[[` or `$`:
+    To extract a single column from a tbl\_df use `[[` or `$`:
     
     ```{r}
     class(df2[[1]])
@@ -211,7 +211,7 @@ Multiple arguments to `filter()` are combined with "and". To get more complicate
 filter(flights, month == 11 | month == 12)
 ```
 
-Note the order isn't like English. This expression doesn't find on months that equal 11 or 12. Instead it finds all months that equal `11 | 12`, which is `TRUE`. In a numeric context (like here), `TRUE` becomes one, so this finds all flights in January, not November or December.
+Note the order isn't like English. The following expression doesn't find on months that equal 11 or 12. Instead it finds all months that equal `11 | 12`, which is `TRUE`. In a numeric context (like here), `TRUE` becomes one, so this finds all flights in January, not November or December.
 
 ```{r, eval = FALSE}
 filter(flights, month == 11 | 12)
@@ -393,7 +393,7 @@ rename(flights, tail_num = tailnum)
 
 --------------------------------------------------------------------------------
 
-This function works similarly to the `select` argument in `base::subset()`. Because the dplyr philosophy is to have small functions that do one thing well, it is its own function in dplyr.
+The `select()` function works similarly to the `select` argument in `base::subset()`. Because the dplyr philosophy is to have small functions that do one thing well, it is its own function in dplyr.
 
 --------------------------------------------------------------------------------
 
@@ -566,7 +566,7 @@ by_day <- group_by(flights, year, month, day)
 summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))
 ```
 
-Together `group_by()` and `summarise()` provide one of tools that you'll use most commonly when working with dplyr: grouped summaries. But before we go any further with this idea, we need to introduce a powerful new idea: the pipe.
+Together `group_by()` and `summarise()` provide one of tools that you'll use most commonly when working with dplyr: grouped summaries. But before we go any further with this, we need to introduce a powerful new idea: the pipe.
 
 ### Combining multiple operations with the pipe
 
@@ -774,7 +774,7 @@ Just using means, counts, and sum can get you a long way, but R provides many ot
     ```
   
 *   By position: `first(x)`, `nth(x, 2)`, `last(x)`. These work similarly to 
-    `x[1]`, `x[length(x)]`, and `x[n]` but let you set a default value if that
+    `x[1]`, x[n], and `x[length(x)]` but let you set a default value if that
     position does not exist (i.e. you're trying to get the 3rd element from a 
     group that only has two elements).